{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "db = json.load(open(r'C:\\Users\\Administrator\\Desktop\\zhihu_data\\user4.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43362"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52353"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "db_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(1,5):\n",
    "    file_name = \"user%s.json\" %i\n",
    "    db_list.extend(json.load(open(os.path.join('C:\\Users\\Administrator\\Desktop\\zhihu_data',file_name))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206007"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(db_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(db_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 206007 entries, 0 to 206006\n",
      "Data columns (total 5 columns):\n",
      "aggree_count       206007 non-null object\n",
      "most_good_topic    206007 non-null object\n",
      "name               206007 non-null object\n",
      "thanks_count       206007 non-null object\n",
      "url                206007 non-null object\n",
      "dtypes: object(5)\n",
      "memory usage: 9.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-daf275b6788b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\pandas\\util\\decorators.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     86\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 88\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     89\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36mdrop_duplicates\u001b[1;34m(self, subset, take_last, inplace)\u001b[0m\n\u001b[0;32m   2826\u001b[0m         \u001b[0mdeduplicated\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2827\u001b[0m         \"\"\"\n\u001b[1;32m-> 2828\u001b[1;33m         \u001b[0mduplicated\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mduplicated\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtake_last\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtake_last\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2829\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2830\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\pandas\\util\\decorators.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     86\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 88\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     89\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36mduplicated\u001b[1;34m(self, subset, take_last)\u001b[0m\n\u001b[0;32m   2871\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2872\u001b[0m         \u001b[0mvals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msubset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2873\u001b[1;33m         \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2874\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2875\u001b[0m         \u001b[0mids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_group_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxnull\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36mf\u001b[1;34m(vals)\u001b[0m\n\u001b[0;32m   2860\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2861\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2862\u001b[1;33m             \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfactorize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize_hint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_SIZE_HINT_LIMIT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2863\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'i8'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2864\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\pandas\\core\\algorithms.pyc\u001b[0m in \u001b[0;36mfactorize\u001b[1;34m(values, sort, order, na_sentinel, size_hint)\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[0mtable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhash_klass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msize_hint\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[0muniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvec_klass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 135\u001b[1;33m     \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muniques\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_sentinel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ensure_platform_int\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_labels (pandas\\hashtable.c:14073)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165877"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.url.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop_duplicates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = df.drop_duplicates('url')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165877"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'aggree_count', u'most_good_topic', u'name', u'thanks_count', u'url'], dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-d425104a2334>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mby\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'aggree_count'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'thanks_count'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mascending\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36msort_index\u001b[1;34m(self, axis, by, ascending, inplace, kind, na_position)\u001b[0m\n\u001b[0;32m   2977\u001b[0m                     \u001b[0mkeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrans\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2978\u001b[0m                 indexer = _lexsort_indexer(keys, orders=ascending,\n\u001b[1;32m-> 2979\u001b[1;33m                                            na_position=na_position)\n\u001b[0m\u001b[0;32m   2980\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ensure_platform_int\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2981\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36m_lexsort_indexer\u001b[1;34m(keys, orders, na_position)\u001b[0m\n\u001b[0;32m   3667\u001b[0m         \u001b[1;31m# create the Categorical\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3668\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3669\u001b[1;33m             \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCategorical\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mordered\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3670\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3671\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mna_position\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'last'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'first'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\pandas\\core\\categorical.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, values, categories, ordered, name, fastpath, levels)\u001b[0m\n\u001b[0;32m    269\u001b[0m                 \u001b[0mcodes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcategories\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfactorize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    270\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 271\u001b[1;33m                 \u001b[0mcodes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcategories\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfactorize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    272\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mordered\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    273\u001b[0m                     \u001b[1;31m# raise, as we don't have a sortable data structure and so the user should\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\pandas\\core\\algorithms.pyc\u001b[0m in \u001b[0;36mfactorize\u001b[1;34m(values, sort, order, na_sentinel, size_hint)\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[0mtable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhash_klass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msize_hint\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[0muniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvec_klass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 135\u001b[1;33m     \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muniques\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_sentinel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ensure_platform_int\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_labels (pandas\\hashtable.c:14073)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "df1.sort_index(by=['aggree_count','thanks_count'],ascending=[False,False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aggree_count</th>\n",
       "      <th>most_good_topic</th>\n",
       "      <th>name</th>\n",
       "      <th>thanks_count</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[136034]</td>\n",
       "      <td>[媒体 ]</td>\n",
       "      <td>[张亮]</td>\n",
       "      <td>[28346]</td>\n",
       "      <td>http://www.zhihu.com/people/imike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[23318]</td>\n",
       "      <td>[首次公开募股（IPO） ]</td>\n",
       "      <td>[Luo Patrick]</td>\n",
       "      <td>[8057]</td>\n",
       "      <td>http://www.zhihu.com/people/patrickluo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[4151]</td>\n",
       "      <td>[商业 ]</td>\n",
       "      <td>[少少]</td>\n",
       "      <td>[1938]</td>\n",
       "      <td>http://www.zhihu.com/people/zliybst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[15476]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[道哥]</td>\n",
       "      <td>[735]</td>\n",
       "      <td>http://www.zhihu.com/people/aullik5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[6295]</td>\n",
       "      <td>[经济学 ]</td>\n",
       "      <td>[冯晗]</td>\n",
       "      <td>[2008]</td>\n",
       "      <td>http://www.zhihu.com/people/feng-han</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[233602]</td>\n",
       "      <td>[社会 ]</td>\n",
       "      <td>[李松蔚]</td>\n",
       "      <td>[43549]</td>\n",
       "      <td>http://www.zhihu.com/people/lisongwei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[1265657]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[张佳玮]</td>\n",
       "      <td>[237486]</td>\n",
       "      <td>http://www.zhihu.com/people/zhang-jia-wei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[19698]</td>\n",
       "      <td>[中国 ]</td>\n",
       "      <td>[sky]</td>\n",
       "      <td>[6795]</td>\n",
       "      <td>http://www.zhihu.com/people/sky-86-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[12181]</td>\n",
       "      <td>[金融工程 ]</td>\n",
       "      <td>[袁浩瀚]</td>\n",
       "      <td>[3507]</td>\n",
       "      <td>http://www.zhihu.com/people/id47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[5707]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[张若鸿]</td>\n",
       "      <td>[745]</td>\n",
       "      <td>http://www.zhihu.com/people/zhang-ruo-hong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aggree_count most_good_topic           name thanks_count  \\\n",
       "0     [136034]           [媒体 ]           [张亮]      [28346]   \n",
       "1      [23318]  [首次公开募股（IPO） ]  [Luo Patrick]       [8057]   \n",
       "2       [4151]           [商业 ]           [少少]       [1938]   \n",
       "3      [15476]              []           [道哥]        [735]   \n",
       "4       [6295]          [经济学 ]           [冯晗]       [2008]   \n",
       "5     [233602]           [社会 ]          [李松蔚]      [43549]   \n",
       "6    [1265657]              []          [张佳玮]     [237486]   \n",
       "7      [19698]           [中国 ]          [sky]       [6795]   \n",
       "8      [12181]         [金融工程 ]          [袁浩瀚]       [3507]   \n",
       "9       [5707]              []          [张若鸿]        [745]   \n",
       "\n",
       "                                          url  \n",
       "0           http://www.zhihu.com/people/imike  \n",
       "1      http://www.zhihu.com/people/patrickluo  \n",
       "2         http://www.zhihu.com/people/zliybst  \n",
       "3         http://www.zhihu.com/people/aullik5  \n",
       "4        http://www.zhihu.com/people/feng-han  \n",
       "5       http://www.zhihu.com/people/lisongwei  \n",
       "6   http://www.zhihu.com/people/zhang-jia-wei  \n",
       "7        http://www.zhihu.com/people/sky-86-5  \n",
       "8            http://www.zhihu.com/people/id47  \n",
       "9  http://www.zhihu.com/people/zhang-ruo-hong  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_int(obj):\n",
    "    obj = ''.join(obj)\n",
    "    obj = obj.encode('utf-8')\n",
    "    return int(obj) if obj else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1.thanks_count = df1.thanks_count.map(get_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     136034\n",
       "1      23318\n",
       "2       4151\n",
       "3      15476\n",
       "4       6295\n",
       "5     233602\n",
       "6    1265657\n",
       "7      19698\n",
       "8      12181\n",
       "9       5707\n",
       "Name: aggree_count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agree[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot convert the series to <type 'int'>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-48-3882211cadbd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0magree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0magree\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\pandas\\core\\series.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     75\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mconverter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m         raise TypeError(\n\u001b[1;32m---> 77\u001b[1;33m             \"cannot convert the series to {0}\".format(str(converter)))\n\u001b[0m\u001b[0;32m     78\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot convert the series to <type 'int'>"
     ]
    }
   ],
   "source": [
    "agree.index(agree==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103003          0\n",
       "104306          0\n",
       "104304          0\n",
       "104302          0\n",
       "104300          0\n",
       "104293          0\n",
       "104289          0\n",
       "104286          0\n",
       "104285          0\n",
       "104279          0\n",
       "104277          0\n",
       "104275          0\n",
       "104273          0\n",
       "104272          0\n",
       "104270          0\n",
       "104269          0\n",
       "104268          0\n",
       "104267          0\n",
       "104266          0\n",
       "104262          0\n",
       "104261          0\n",
       "104260          0\n",
       "104307          0\n",
       "104258          0\n",
       "104313          0\n",
       "104320          0\n",
       "104373          0\n",
       "104371          0\n",
       "104368          0\n",
       "104366          0\n",
       "           ...   \n",
       "130296     240410\n",
       "124226     242486\n",
       "25835      242507\n",
       "121389     246911\n",
       "57128      247408\n",
       "117635     247408\n",
       "14054      247408\n",
       "167846     247408\n",
       "165212     252262\n",
       "13038      261356\n",
       "150        270619\n",
       "162774     270666\n",
       "170865     272164\n",
       "109250     276975\n",
       "164714     311938\n",
       "6643       314427\n",
       "193375     314428\n",
       "138743     314428\n",
       "116578     314429\n",
       "164313     318960\n",
       "40193      318960\n",
       "118891     339023\n",
       "117713     346094\n",
       "111128     368292\n",
       "118749     406651\n",
       "53544      499498\n",
       "288        532322\n",
       "172724     653899\n",
       "130644     653903\n",
       "6         1265657\n",
       "Name: aggree_count, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agree.order()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 165877 entries, 0 to 206006\n",
      "Data columns (total 5 columns):\n",
      "aggree_count       165877 non-null int64\n",
      "most_good_topic    165877 non-null object\n",
      "name               165877 non-null object\n",
      "thanks_count       165877 non-null int64\n",
      "url                165877 non-null object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = (df1.sort_index(by=['aggree_count','thanks_count'],ascending=[False,False]))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "_ = df2.set_index('name',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aggree_count</th>\n",
       "      <th>most_good_topic</th>\n",
       "      <th>thanks_count</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[张佳玮]</th>\n",
       "      <td>1265657</td>\n",
       "      <td>[]</td>\n",
       "      <td>237486</td>\n",
       "      <td>http://www.zhihu.com/people/zhang-jia-wei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[朱炫]</th>\n",
       "      <td>653903</td>\n",
       "      <td>[幽默 ]</td>\n",
       "      <td>144548</td>\n",
       "      <td>http://www.zhihu.com/people/zhu-xuan-86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[肥肥猫]</th>\n",
       "      <td>532322</td>\n",
       "      <td>[调查类问题 ]</td>\n",
       "      <td>135355</td>\n",
       "      <td>http://www.zhihu.com/people/feifeimao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[yolfilm]</th>\n",
       "      <td>499498</td>\n",
       "      <td>[]</td>\n",
       "      <td>119908</td>\n",
       "      <td>http://www.zhihu.com/people/yolfilm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[采铜]</th>\n",
       "      <td>406651</td>\n",
       "      <td>[]</td>\n",
       "      <td>98384</td>\n",
       "      <td>http://www.zhihu.com/people/cai-tong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[Kaiser]</th>\n",
       "      <td>368292</td>\n",
       "      <td>[]</td>\n",
       "      <td>53706</td>\n",
       "      <td>http://www.zhihu.com/people/kaiserwang730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[一笑风云过]</th>\n",
       "      <td>346094</td>\n",
       "      <td>[生活 ]</td>\n",
       "      <td>55437</td>\n",
       "      <td>http://www.zhihu.com/people/yixiao-feng-yun-guo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[vczh]</th>\n",
       "      <td>339023</td>\n",
       "      <td>[计算机图形学 ]</td>\n",
       "      <td>44968</td>\n",
       "      <td>http://www.zhihu.com/people/zord-vczh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[Justin Lee]</th>\n",
       "      <td>318960</td>\n",
       "      <td>[人际交往 ]</td>\n",
       "      <td>71049</td>\n",
       "      <td>http://www.zhihu.com/people/lzy135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[李嫑嫑]</th>\n",
       "      <td>314427</td>\n",
       "      <td>[音乐 ]</td>\n",
       "      <td>54576</td>\n",
       "      <td>http://www.zhihu.com/people/linaoshi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              aggree_count most_good_topic  thanks_count  \\\n",
       "name                                                       \n",
       "[张佳玮]              1265657              []        237486   \n",
       "[朱炫]                653903           [幽默 ]        144548   \n",
       "[肥肥猫]               532322        [调查类问题 ]        135355   \n",
       "[yolfilm]           499498              []        119908   \n",
       "[采铜]                406651              []         98384   \n",
       "[Kaiser]            368292              []         53706   \n",
       "[一笑风云过]             346094           [生活 ]         55437   \n",
       "[vczh]              339023       [计算机图形学 ]         44968   \n",
       "[Justin Lee]        318960         [人际交往 ]         71049   \n",
       "[李嫑嫑]               314427           [音乐 ]         54576   \n",
       "\n",
       "                                                          url  \n",
       "name                                                           \n",
       "[张佳玮]               http://www.zhihu.com/people/zhang-jia-wei  \n",
       "[朱炫]                  http://www.zhihu.com/people/zhu-xuan-86  \n",
       "[肥肥猫]                   http://www.zhihu.com/people/feifeimao  \n",
       "[yolfilm]                 http://www.zhihu.com/people/yolfilm  \n",
       "[采铜]                     http://www.zhihu.com/people/cai-tong  \n",
       "[Kaiser]            http://www.zhihu.com/people/kaiserwang730  \n",
       "[一笑风云过]       http://www.zhihu.com/people/yixiao-feng-yun-guo  \n",
       "[vczh]                  http://www.zhihu.com/people/zord-vczh  \n",
       "[Justin Lee]               http://www.zhihu.com/people/lzy135  \n",
       "[李嫑嫑]                    http://www.zhihu.com/people/linaoshi  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x296c93c8>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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XDrCkxkj6ou2zm65jUGbSN5LjyCPgHyuPd9HMTW6a9BlJ88c2SnovcH0D9eyz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\nEVHr/wMac0Axcky5owAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29756cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['aggree_count'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_str(obj):\n",
    "    obj = ''.join(obj)\n",
    "    #obj = obj.encode('utf-8')\n",
    "    return obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2.index = df2.index.map(get_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['\\xe5\\xbc\\xa0\\xe4\\xbd\\xb3\\xe7\\x8e\\xae' '\\xe6\\x9c\\xb1\\xe7\\x82\\xab'\n",
      " '\\xe8\\x82\\xa5\\xe8\\x82\\xa5\\xe7\\x8c\\xab' 'yolfilm'\n",
      " '\\xe9\\x87\\x87\\xe9\\x93\\x9c' 'Kaiser'\n",
      " '\\xe4\\xb8\\x80\\xe7\\xac\\x91\\xe9\\xa3\\x8e\\xe4\\xba\\x91\\xe8\\xbf\\x87' 'vczh'\n",
      " 'Justin Lee' '\\xe6\\x9d\\x8e\\xe5\\xab\\x91\\xe5\\xab\\x91']\n"
     ]
    }
   ],
   "source": [
    "print index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([       u'张佳玮',         u'朱炫',        u'肥肥猫',    u'yolfilm',\n",
       "               u'采铜',     u'Kaiser',      u'一笑风云过',       u'vczh',\n",
       "       u'Justin Lee',        u'李嫑嫑'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.font_manager import FontProperties "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "font = FontProperties(fname=r\"c:\\windows\\fonts\\simsun.ttc\", size=14) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LC8uWCwoKKCgo2GV1cbsT/z13t/coKiqiqKgo63y5tjyOAU6RdDKwL9BU0v3A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\nzYOHc865rP0/sZzCz0RYKNYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250aa278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df2.plot(kind = 'bar', rot = 0) \n",
    "for label in ax.get_xticklabels(): \n",
    "    label.set_fontproperties(font) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.show?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "show() got an unexpected keyword argument 'size'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-79-cbd2d296a3c7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\anaconda2.7\\lib\\site-packages\\matplotlib\\pyplot.pyc\u001b[0m in \u001b[0;36mshow\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m    153\u001b[0m     \"\"\"\n\u001b[0;32m    154\u001b[0m     \u001b[1;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: show() got an unexpected keyword argument 'size'"
     ]
    }
   ],
   "source": [
    "plt.show(size=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame,Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "db_list = []  #存放用户信息列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json,os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(1,21):  #获取文件夹下面的20个用户文件，命名分别为user1,user2...user20\n",
    "    file_name = \"user%s.json\" %i\n",
    "    db_list.extend(json.load(open(os.path.join('C:\\Users\\Administrator\\Desktop\\zhihu_data',file_name))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1051127"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(db_list)    #总计共有100多w条用户信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'aggree_count': [u'136034'],\n",
       " u'most_good_topic': [u'\\u5a92\\u4f53 '],\n",
       " u'name': [u'\\u5f20\\u4eae'],\n",
       " u'thanks_count': [u'28346'],\n",
       " u'url': u'http://www.zhihu.com/people/imike'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_list[0]   #db_list存放用户信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = DataFrame(db_list)     #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aggree_count</th>\n",
       "      <th>most_good_topic</th>\n",
       "      <th>name</th>\n",
       "      <th>thanks_count</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[136034]</td>\n",
       "      <td>[媒体 ]</td>\n",
       "      <td>[张亮]</td>\n",
       "      <td>[28346]</td>\n",
       "      <td>http://www.zhihu.com/people/imike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[23318]</td>\n",
       "      <td>[首次公开募股（IPO） ]</td>\n",
       "      <td>[Luo Patrick]</td>\n",
       "      <td>[8057]</td>\n",
       "      <td>http://www.zhihu.com/people/patrickluo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[4151]</td>\n",
       "      <td>[商业 ]</td>\n",
       "      <td>[少少]</td>\n",
       "      <td>[1938]</td>\n",
       "      <td>http://www.zhihu.com/people/zliybst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[15476]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[道哥]</td>\n",
       "      <td>[735]</td>\n",
       "      <td>http://www.zhihu.com/people/aullik5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[6295]</td>\n",
       "      <td>[经济学 ]</td>\n",
       "      <td>[冯晗]</td>\n",
       "      <td>[2008]</td>\n",
       "      <td>http://www.zhihu.com/people/feng-han</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[233602]</td>\n",
       "      <td>[社会 ]</td>\n",
       "      <td>[李松蔚]</td>\n",
       "      <td>[43549]</td>\n",
       "      <td>http://www.zhihu.com/people/lisongwei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[1265657]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[张佳玮]</td>\n",
       "      <td>[237486]</td>\n",
       "      <td>http://www.zhihu.com/people/zhang-jia-wei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[19698]</td>\n",
       "      <td>[中国 ]</td>\n",
       "      <td>[sky]</td>\n",
       "      <td>[6795]</td>\n",
       "      <td>http://www.zhihu.com/people/sky-86-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[12181]</td>\n",
       "      <td>[金融工程 ]</td>\n",
       "      <td>[袁浩瀚]</td>\n",
       "      <td>[3507]</td>\n",
       "      <td>http://www.zhihu.com/people/id47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[5707]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[张若鸿]</td>\n",
       "      <td>[745]</td>\n",
       "      <td>http://www.zhihu.com/people/zhang-ruo-hong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aggree_count most_good_topic           name thanks_count  \\\n",
       "0     [136034]           [媒体 ]           [张亮]      [28346]   \n",
       "1      [23318]  [首次公开募股（IPO） ]  [Luo Patrick]       [8057]   \n",
       "2       [4151]           [商业 ]           [少少]       [1938]   \n",
       "3      [15476]              []           [道哥]        [735]   \n",
       "4       [6295]          [经济学 ]           [冯晗]       [2008]   \n",
       "5     [233602]           [社会 ]          [李松蔚]      [43549]   \n",
       "6    [1265657]              []          [张佳玮]     [237486]   \n",
       "7      [19698]           [中国 ]          [sky]       [6795]   \n",
       "8      [12181]         [金融工程 ]          [袁浩瀚]       [3507]   \n",
       "9       [5707]              []          [张若鸿]        [745]   \n",
       "\n",
       "                                          url  \n",
       "0           http://www.zhihu.com/people/imike  \n",
       "1      http://www.zhihu.com/people/patrickluo  \n",
       "2         http://www.zhihu.com/people/zliybst  \n",
       "3         http://www.zhihu.com/people/aullik5  \n",
       "4        http://www.zhihu.com/people/feng-han  \n",
       "5       http://www.zhihu.com/people/lisongwei  \n",
       "6   http://www.zhihu.com/people/zhang-jia-wei  \n",
       "7        http://www.zhihu.com/people/sky-86-5  \n",
       "8            http://www.zhihu.com/people/id47  \n",
       "9  http://www.zhihu.com/people/zhang-ruo-hong  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.drop_duplicates('url')      #对数据去重，这里选取url作为去重的标准，因为对于一个用户来说，url相同，那么其他信息也肯定相同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "445295"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)                             #经过去去重后，我们剩下了45w不同用户的信息（蛋疼，多进程爬虫带来速度上的提升，也带来了重合）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_int(obj): #将df的aggree_count和thanks_count列由list类型转化成int类型\n",
    "    obj = ''.join(obj)    #list to string\n",
    "    obj = obj.encode('utf-8')  #unicode to utf-8\n",
    "    return int(obj) if obj else 0 #最后如果obj为空字符串，return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.aggree_count = df.aggree_count.map(get_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.thanks_count = df.thanks_count.map(get_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aggree_count</th>\n",
       "      <th>most_good_topic</th>\n",
       "      <th>name</th>\n",
       "      <th>thanks_count</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136034</td>\n",
       "      <td>[媒体 ]</td>\n",
       "      <td>[张亮]</td>\n",
       "      <td>28346</td>\n",
       "      <td>http://www.zhihu.com/people/imike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23318</td>\n",
       "      <td>[首次公开募股（IPO） ]</td>\n",
       "      <td>[Luo Patrick]</td>\n",
       "      <td>8057</td>\n",
       "      <td>http://www.zhihu.com/people/patrickluo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4151</td>\n",
       "      <td>[商业 ]</td>\n",
       "      <td>[少少]</td>\n",
       "      <td>1938</td>\n",
       "      <td>http://www.zhihu.com/people/zliybst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15476</td>\n",
       "      <td>[]</td>\n",
       "      <td>[道哥]</td>\n",
       "      <td>735</td>\n",
       "      <td>http://www.zhihu.com/people/aullik5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6295</td>\n",
       "      <td>[经济学 ]</td>\n",
       "      <td>[冯晗]</td>\n",
       "      <td>2008</td>\n",
       "      <td>http://www.zhihu.com/people/feng-han</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>233602</td>\n",
       "      <td>[社会 ]</td>\n",
       "      <td>[李松蔚]</td>\n",
       "      <td>43549</td>\n",
       "      <td>http://www.zhihu.com/people/lisongwei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1265657</td>\n",
       "      <td>[]</td>\n",
       "      <td>[张佳玮]</td>\n",
       "      <td>237486</td>\n",
       "      <td>http://www.zhihu.com/people/zhang-jia-wei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19698</td>\n",
       "      <td>[中国 ]</td>\n",
       "      <td>[sky]</td>\n",
       "      <td>6795</td>\n",
       "      <td>http://www.zhihu.com/people/sky-86-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12181</td>\n",
       "      <td>[金融工程 ]</td>\n",
       "      <td>[袁浩瀚]</td>\n",
       "      <td>3507</td>\n",
       "      <td>http://www.zhihu.com/people/id47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5707</td>\n",
       "      <td>[]</td>\n",
       "      <td>[张若鸿]</td>\n",
       "      <td>745</td>\n",
       "      <td>http://www.zhihu.com/people/zhang-ruo-hong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aggree_count most_good_topic           name  thanks_count  \\\n",
       "0        136034           [媒体 ]           [张亮]         28346   \n",
       "1         23318  [首次公开募股（IPO） ]  [Luo Patrick]          8057   \n",
       "2          4151           [商业 ]           [少少]          1938   \n",
       "3         15476              []           [道哥]           735   \n",
       "4          6295          [经济学 ]           [冯晗]          2008   \n",
       "5        233602           [社会 ]          [李松蔚]         43549   \n",
       "6       1265657              []          [张佳玮]        237486   \n",
       "7         19698           [中国 ]          [sky]          6795   \n",
       "8         12181         [金融工程 ]          [袁浩瀚]          3507   \n",
       "9          5707              []          [张若鸿]           745   \n",
       "\n",
       "                                          url  \n",
       "0           http://www.zhihu.com/people/imike  \n",
       "1      http://www.zhihu.com/people/patrickluo  \n",
       "2         http://www.zhihu.com/people/zliybst  \n",
       "3         http://www.zhihu.com/people/aullik5  \n",
       "4        http://www.zhihu.com/people/feng-han  \n",
       "5       http://www.zhihu.com/people/lisongwei  \n",
       "6   http://www.zhihu.com/people/zhang-jia-wei  \n",
       "7        http://www.zhihu.com/people/sky-86-5  \n",
       "8            http://www.zhihu.com/people/id47  \n",
       "9  http://www.zhihu.com/people/zhang-ruo-hong  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:10]    #aggree_count和thanks_count都转化成int类型啦"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_str(obj):  #将most_good_topic和thanks_count由list转化成str\n",
    "    obj = ''.join(obj)\n",
    "    return obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.most_good_topic = df.most_good_topic.map(get_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.name = df.name.map(get_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aggree_count</th>\n",
       "      <th>most_good_topic</th>\n",
       "      <th>name</th>\n",
       "      <th>thanks_count</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136034</td>\n",
       "      <td>媒体</td>\n",
       "      <td>张亮</td>\n",
       "      <td>28346</td>\n",
       "      <td>http://www.zhihu.com/people/imike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23318</td>\n",
       "      <td>首次公开募股（IPO）</td>\n",
       "      <td>Luo Patrick</td>\n",
       "      <td>8057</td>\n",
       "      <td>http://www.zhihu.com/people/patrickluo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4151</td>\n",
       "      <td>商业</td>\n",
       "      <td>少少</td>\n",
       "      <td>1938</td>\n",
       "      <td>http://www.zhihu.com/people/zliybst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15476</td>\n",
       "      <td></td>\n",
       "      <td>道哥</td>\n",
       "      <td>735</td>\n",
       "      <td>http://www.zhihu.com/people/aullik5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6295</td>\n",
       "      <td>经济学</td>\n",
       "      <td>冯晗</td>\n",
       "      <td>2008</td>\n",
       "      <td>http://www.zhihu.com/people/feng-han</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>233602</td>\n",
       "      <td>社会</td>\n",
       "      <td>李松蔚</td>\n",
       "      <td>43549</td>\n",
       "      <td>http://www.zhihu.com/people/lisongwei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1265657</td>\n",
       "      <td></td>\n",
       "      <td>张佳玮</td>\n",
       "      <td>237486</td>\n",
       "      <td>http://www.zhihu.com/people/zhang-jia-wei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19698</td>\n",
       "      <td>中国</td>\n",
       "      <td>sky</td>\n",
       "      <td>6795</td>\n",
       "      <td>http://www.zhihu.com/people/sky-86-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12181</td>\n",
       "      <td>金融工程</td>\n",
       "      <td>袁浩瀚</td>\n",
       "      <td>3507</td>\n",
       "      <td>http://www.zhihu.com/people/id47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5707</td>\n",
       "      <td></td>\n",
       "      <td>张若鸿</td>\n",
       "      <td>745</td>\n",
       "      <td>http://www.zhihu.com/people/zhang-ruo-hong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aggree_count most_good_topic         name  thanks_count  \\\n",
       "0        136034             媒体            张亮         28346   \n",
       "1         23318    首次公开募股（IPO）   Luo Patrick          8057   \n",
       "2          4151             商业            少少          1938   \n",
       "3         15476                           道哥           735   \n",
       "4          6295            经济学            冯晗          2008   \n",
       "5        233602             社会           李松蔚         43549   \n",
       "6       1265657                          张佳玮        237486   \n",
       "7         19698             中国           sky          6795   \n",
       "8         12181           金融工程           袁浩瀚          3507   \n",
       "9          5707                          张若鸿           745   \n",
       "\n",
       "                                          url  \n",
       "0           http://www.zhihu.com/people/imike  \n",
       "1      http://www.zhihu.com/people/patrickluo  \n",
       "2         http://www.zhihu.com/people/zliybst  \n",
       "3         http://www.zhihu.com/people/aullik5  \n",
       "4        http://www.zhihu.com/people/feng-han  \n",
       "5       http://www.zhihu.com/people/lisongwei  \n",
       "6   http://www.zhihu.com/people/zhang-jia-wei  \n",
       "7        http://www.zhihu.com/people/sky-86-5  \n",
       "8            http://www.zhihu.com/people/id47  \n",
       "9  http://www.zhihu.com/people/zhang-ruo-hong  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#找出这45w用户中，收到的点赞数最多的人,赶紧去看看你们心目中的知乎大神吧，现在知道要关注谁了吧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aggree_count</th>\n",
       "      <th>most_good_topic</th>\n",
       "      <th>name</th>\n",
       "      <th>thanks_count</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1265657</td>\n",
       "      <td></td>\n",
       "      <td>张佳玮</td>\n",
       "      <td>237486</td>\n",
       "      <td>http://www.zhihu.com/people/zhang-jia-wei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150180</th>\n",
       "      <td>653903</td>\n",
       "      <td>幽默</td>\n",
       "      <td>朱炫</td>\n",
       "      <td>144548</td>\n",
       "      <td>http://www.zhihu.com/people/zhu-xuan-86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>532322</td>\n",
       "      <td>调查类问题</td>\n",
       "      <td>肥肥猫</td>\n",
       "      <td>135355</td>\n",
       "      <td>http://www.zhihu.com/people/feifeimao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61837</th>\n",
       "      <td>499498</td>\n",
       "      <td></td>\n",
       "      <td>yolfilm</td>\n",
       "      <td>119908</td>\n",
       "      <td>http://www.zhihu.com/people/yolfilm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490241</th>\n",
       "      <td>421979</td>\n",
       "      <td></td>\n",
       "      <td>GayScript</td>\n",
       "      <td>88273</td>\n",
       "      <td>http://www.zhihu.com/people/gayscript</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415540</th>\n",
       "      <td>406983</td>\n",
       "      <td>美食</td>\n",
       "      <td>谢熊猫君</td>\n",
       "      <td>92042</td>\n",
       "      <td>http://www.zhihu.com/people/xiepanda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138285</th>\n",
       "      <td>406651</td>\n",
       "      <td></td>\n",
       "      <td>采铜</td>\n",
       "      <td>98384</td>\n",
       "      <td>http://www.zhihu.com/people/cai-tong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394719</th>\n",
       "      <td>369007</td>\n",
       "      <td></td>\n",
       "      <td>菠菜</td>\n",
       "      <td>103076</td>\n",
       "      <td>http://www.zhihu.com/people/bo-cai-28-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119421</th>\n",
       "      <td>368292</td>\n",
       "      <td></td>\n",
       "      <td>Kaiser</td>\n",
       "      <td>53706</td>\n",
       "      <td>http://www.zhihu.com/people/kaiserwang730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879559</th>\n",
       "      <td>361997</td>\n",
       "      <td>恋爱</td>\n",
       "      <td>王豖</td>\n",
       "      <td>77119</td>\n",
       "      <td>http://www.zhihu.com/people/WxzxzW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        aggree_count most_good_topic       name  thanks_count  \\\n",
       "6            1265657                        张佳玮        237486   \n",
       "150180        653903             幽默          朱炫        144548   \n",
       "288           532322          调查类问题         肥肥猫        135355   \n",
       "61837         499498                    yolfilm        119908   \n",
       "490241        421979                  GayScript         88273   \n",
       "415540        406983             美食        谢熊猫君         92042   \n",
       "138285        406651                         采铜         98384   \n",
       "394719        369007                         菠菜        103076   \n",
       "119421        368292                     Kaiser         53706   \n",
       "879559        361997             恋爱          王豖         77119   \n",
       "\n",
       "                                              url  \n",
       "6       http://www.zhihu.com/people/zhang-jia-wei  \n",
       "150180    http://www.zhihu.com/people/zhu-xuan-86  \n",
       "288         http://www.zhihu.com/people/feifeimao  \n",
       "61837         http://www.zhihu.com/people/yolfilm  \n",
       "490241      http://www.zhihu.com/people/gayscript  \n",
       "415540       http://www.zhihu.com/people/xiepanda  \n",
       "138285       http://www.zhihu.com/people/cai-tong  \n",
       "394719    http://www.zhihu.com/people/bo-cai-28-7  \n",
       "119421  http://www.zhihu.com/people/kaiserwang730  \n",
       "879559         http://www.zhihu.com/people/WxzxzW  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(by='aggree_count',ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#下面为了更直观看出各位大神的数据量，我采用图标来表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "from matplotlib.font_manager import FontProperties \n",
    "font = FontProperties(fname=r\"c:\\windows\\fonts\\simsun.ttc\", size=10)  #size可不用指定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_name = df.sort_index(by='aggree_count',ascending=False)[:10].set_index('name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8mG261jBdu9dm39pVV1dTXV1d9Limliw+Axwr6fPA9kAXSbcASyXtFBFLchXT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13453eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_name.plot(kind = 'bar') \n",
    "for label in ax.get_xticklabels() : \n",
    "    label.set_fontproperties(font) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_topic = df[df.most_good_topic!='']   #去掉那些没有擅长领域的用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32998"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_topic)  #看来知乎网的用户活跃度还是不高的，45w用户中，只有3w多一些有自己擅长的话题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "互联网       746\n",
       "电影        553\n",
       "生活        500\n",
       "历史        411\n",
       "文学        320\n",
       "心理学       311\n",
       "音乐        299\n",
       "游戏        298\n",
       "摄影        296\n",
       "调查类问题     243\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_topic.most_good_topic.value_counts()[:10]  #看来如果你发起一个话题，那么我建议你选择互联网方面的，那么它的流行度会大一些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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